This application proposes an Academic-Industrial Partnership (AIP) consisting of Riverside Research (RR) in New York, NY, as the primary partner in collaboration with the GE Global Research Center (GE) in Niskayuna, NY, as the industrial partner, and with the Stony Brook Medical (SBM) affiliated with the State University of New York in Stony Brook, NY, and the Kuakini Medical Center (KMC) affiliated with the University of Hawaii in Honolulu, HI, as the clinical/academic partners. The proposed project addresses the need for reliable, highly sensitive means of detecting metastases to lymph nodes (LNs) in order to correctly stage and optimally treat cancer. Accordingly, we seek to validate existing encouraging results obtained by RR and KMC using quantitative-ultrasound (QUS) methods for detecting metastases in LNs by applying and evaluating these promising methods using a far larger number of LNs and including a far broader range of cancer types from a more-diverse patient population than has been possible in studies to date. Validation efforts will include development, deployment and evaluation of a pre-prototype ultrasonic instrument for QUS-based detection and imaging of metastases in the pathology laboratory. Successful validation will establish a foundation for translation of the QUS methods into a compact, low-cost instrument for metastases detection in dissected LNs to be used in the pathology laboratory or possibly in the operating room. Successful validation also will establish the foundation for a subsequent instrument that can detect and image nodal metastases intra-operatively in the operating room or pre-operatively in the examination room. The AIP partners will synergize capabilities in the following general ways: RR will provide project oversight and coordination and will apply, refine, and test the QUS methods; GE will provide ultrasound (US) instrumentation by designing, fabricating, and characterizing a pre-prototype device; SBM and KMC will provide clinical support by recruiting patients and acquiring US and histological data from dissected LNs.